User Cognitive Abilities-Human Computer Interaction Tasks Model

Shi, Jinshou; Tang, Wenzhe; Li, Ning; Zhou, Yingwei; Zhou, Tuoyang; Chen, Ziang; Yin, Kaili

doi:10.1007/978-3-030-68017-6_29

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Human factors research has shown that supplementing traditionally qualitative methodologies with high-rate quantitative data inputs both reduces design cycle time and allows for improved product design (Johnston et al, 2022). It is not only important to capture track hands spatially and to a high degree of accuracy but to also classify what the hands and/or activity the individual is doing when interacting with the product (Joong Hee Lee & Yun, 2023;Shi et al, 2021). Higher-level classifications, such as therbligs, can help synthesise the system of interactions that an individual has performed.…”

Section: Figure 1 Hand Interaction Across Design Manufacturing and Pr...mentioning

confidence: 99%

A low-cost non-intrusive spatial hand tracking pipeline for product-process interaction

Gopsill,

Kukreja,

Cox

et al. 2024

Proc. Des. Soc.

View full text Add to dashboard Cite

Hands are the sensors and actuators for many design tasks. While several tools exist to capture human interaction and pose, many are expensive and require intrusive measurement devices to be placed on participants and often takes them out of the natural working environment. This paper reports a novel workflow that combines computer vision, several Machine Learning algorithms, and geometric transformations to provide a low-cost non-intrusive means of spatially tracking hands. A ±3mm position accuracy was attained across a series of 3-dimensional follow the path studies.

show abstract

Section: Figure 1 Hand Interaction Across Design Manufacturing and Pr...mentioning

confidence: 99%

A low-cost non-intrusive spatial hand tracking pipeline for product-process interaction

Gopsill,

Kukreja,

Cox

et al. 2024

Proc. Des. Soc.

View full text Add to dashboard Cite

show abstract

“…The research team established the HMI software development and operation, software architecture, basic content and function, and interaction process. On the basis of these field investigations and post-interviews about operator cognitive abilities [39], according to HMI software main interface function division as shown in Figure 2 and operator routine interaction behavior, we finally extracted five types of typical HCI tasks: ① Generalized browse task: Daily production monitoring, performing aimless visual browsing behavior. ② Visual search task: Visual positioning of specific targets such as equipment operation state and production data, abnormal target and early warning Table review task requires the operator to find specific targets in the table, visually locate and report data, and perform visually locate targets and search reporting behavior.…”

Section: Extract Typical Hci Implicit Intentmentioning

confidence: 99%

Human–Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding

Miao,

Hou

2023

Applied Sciences

View full text Add to dashboard Cite

In the short term, a fully autonomous level of machine intelligence cannot be achieved. Humans are still an important part of HCI systems, and intelligent systems should be able to “feel” and “predict” human intentions in order to achieve dynamic coordination between humans and machines. Intent recognition is very important to improve the accuracy and efficiency of the HCI system. However, it is far from enough to focus only on explicit intent. There is a lot of vague and hidden implicit intent in the process of human–computer interaction. Based on passive brain–computer interface (pBCI) technology, this paper proposes a method to integrate humans into HCI systems naturally, which is to establish an intent-based HCI model and automatically recognize the implicit intent according to human EEG signals. In view of the existing problems of few divisible patterns and low efficiency of implicit intent recognition, this paper finally proves that EEG can be used as the basis for judging human implicit intent through extracting multi-task intention, carrying out experiments, and constructing algorithmic models. The CSP + SVM algorithm model can effectively improve the EEG decoding performance of implicit intent in HCI, and the effectiveness of the CSP algorithm on intention feature extraction is further verified by combining 3D space visualization. The translation of implicit intent information is of significance for the study of intent-based HCI models, the development of HCI systems, and the improvement of human–machine collaboration efficiency.

show abstract

Cognitive abilities and visual complexity impact first impressions in five-second testing

Kuric,

Demcak,

Krajcovic

et al. 2023

Behaviour & Information Technology

View full text Add to dashboard Cite

User Cognitive Abilities-Human Computer Interaction Tasks Model

Cited by 3 publications

References 8 publications

A low-cost non-intrusive spatial hand tracking pipeline for product-process interaction

A low-cost non-intrusive spatial hand tracking pipeline for product-process interaction

Human–Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding

Cognitive abilities and visual complexity impact first impressions in five-second testing

Contact Info

Product

Resources

About